Applied Machine Learning Scientist II
An early-career ML scientist at OpenTable prototypes and productionizes machine learning systems for restaurant content understanding, retrieval, matching, ranking, and recommendations, using Python, deep learning frameworks like PyTorch, and LLMs. It's a hybrid role in Toronto (2 days/week in office), building scalable data/ML pipelines and evaluation frameworks.
- Compensation
- CA$170K–CA$190K / yr
- Location
- Toronto, Canada
- Work setup
- Hybrid
- Seniority
- Middle
- Employment
- Not specified
- Experience
- 0+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's, Master's, or PhD in Computer Science, Statistics, Mathematics, or related technical field with 0-3 years of relevant academic or professional experience
- Proficiency in Python and foundational machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost)
- Hands-on experience training, tuning, evaluating, and debugging classical machine learning and deep learning models, including Transformers
- Strong foundation in software engineering principles, algorithms, and data structures, with familiarity in large-scale data systems
- Demonstrated ability to translate business goals into ML metrics and address production issues like data drift and overfitting
Nice to have
- Experience applying machine learning to text, image, or multimodal content for retrieval, matching, ranking, or recommendation
- Familiarity with large language model (LLM) workflows for text and image processing tasks
- Exposure to search systems, information retrieval, embeddings, or learning-to-rank methodologies
- Experience with human-in-the-loop machine learning systems supporting operational or editorial workflows
- Demonstrated applied curiosity through substantial technical projects, research, open-source contributions, or internships
Full description
This hybrid role requires working in the office two days per week.
With millions of diners, 70,000+ restaurant partners and 25+ years of experience, OpenTable, part of Booking Holdings, Inc. (NASDAQ: BKNG), is an industry leader with a passion for helping restaurants thrive. Our world-class technology empowers restaurants to focus on what matters most – their team, their guests, and their bottom line – while enabling diners to discover and book the perfect restaurant for every occasion.
Every employee at OpenTable has a tangible impact on what we do and how we do it. You’ll also be part of a global team and its portfolio of metasearch brands. Hospitality is all about taking care of others, and it defines our culture.
Why this role at OpenTable?
OpenTable seats 25 million diners each month across 70 000+ restaurants and taps into more than 20 years of booking data—an ideal launchpad for an early‑career ML scientist. In our tight‑knit team, every experiment you run and every model you ship goes live quickly and at scale. You’ll start with well‑scoped projects and close mentorship, then rapidly earn the freedom to pitch and implement research ideas that deliver measurable value for diners, restaurants, and the business. We celebrate agency, speed, and relentless experimentation, balanced by disciplined prioritization rooted in ML expertise and real‑world production and business constraints. This posting is for an existing vacancy.
Responsibilities
- Prototype, evaluate, and productionize machine learning systems to enhance restaurant content understanding, retrieval, matching, ranking, and recommendation.
- Build and monitor scalable data and machine learning pipelines, ensuring strong data quality and reproducibility.
- Design evaluation frameworks and experiments to define success metrics and continuously improve model quality, relevance, and product impact.
- Apply large language models (LLMs) and multimodal approaches to content tasks like extraction and classification while optimizing for performance, latency, and cost.
- Develop reusable machine learning tooling and maintain rigorous standards for code quality, deployment, and documentation.
- Collaborate cross-functionally with engineering, product, and content teams to translate ambiguous business needs into practical, scalable machine learning solutions.
Minimum Qualifications
- Bachelor's, Master's, or PhD degree in Computer Science, Statistics, Mathematics, or a related technical field, with 0-3 years of relevant academic or professional experience.
- Proficiency in Python and foundational machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost).